Chaos: A new mechanism for enhancing the optical generation rate in optically thin solar cells
Bibliographic record
Abstract
The photogenerated current of solar cells can be enhanced by light management with surface structures. For solar cells with optically thin absorbing layers, it is especially important to take advantage of this fact through light trapping. The general idea behind light trapping is to use structures, either on the front surface or on the back, to scatter light rays to maximize their path length in the absorber. In this paper, we investigate the potential of chaotic scattering for light trapping. It is well known that the trajectories close to the invariant set of a chaotic scatterer spend a very long time inside of the scatterer before they leave. The invariant set, also called the chaotic repeller, contains all rays of infinite length that never enter or leave the region of the scatterer. If chaotic repellers exist in a system, a chaotic dynamics is present in the scatterer. As a model system, we investigate an elliptical dome structure placed on top of an optically thin absorbing film, a system inspired by the chaotic Bunimovich stadium. A classical ray-tracing program has been developed to classify the scattering dynamics and to evaluate the absorption efficiency, modeled with Beer-Lambert's law. We find that there is a strong correlation between the enhancement of absorption efficiency and the onset of chaotic scattering in such systems. The dynamics of the systems was shown to be chaotic by their positive Lyapunov exponents and the noninteger fractal dimension of their scattering fractals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".